Learning to Corrupt for Better Restoration

Recalling that diffusion models map noisy images to cleanones, the choice of noise addition (i.e., corruption) critically influencesthe denoising process. However, prior diffusion-based image restoration (IR)methods often employ naïve corruption strategies—such as randomlysampling noise, fixing the timestep, or even omitting corruption alto-gether—which may not accurately reflect the actual degradation of eachimage. To handle this, we propose Input-Aware Corruption for IR (IAC-IR) framework, which maps each low-quality (LQ) input into an optimalnoisy sample that lies on the pretrained diffusion trajectory. Instead ofchoosing corruption heuristically, we predict the timestep and noise us-ing supervision derived from the properties of the pretrained diffusionmodel. Specifically, the predicted timestep aligns the corrupted samplewith the Gaussian noise corrupted distribution, while the predicted noisepreserves the recoverable content of the input. Moreover, we use theseinput-aware corruption factors to improve conventional score-based dis-tillation. Rather than relying on random corruption, which often produceunreliable target scores and weak gradients, we perform distillation withinput-aware corruption, yielding more reliable score estimates and morestable distillation. By modeling input-aware corruption and integratingit into distillation, our method better leverages the pretrained diffusionprior, achieving superior perceptual quality in image restoration.